10 Machine Learning Startups Transforming Their Industries
From agriculture to cybersecurity, machine learning startups are changing the world
Artificial intelligence is one of the technologies with the most transformative potential in business. According to research by McKinsey, 70 per cent of companies are likely to have adopted at least one form of AI by 2030. This will contribute to an additional $13tr of global economic activity.
Machine learning – a subset of artificial intelligence – enables machines to get better at executing tasks without human intervention, by finding patterns in data, and learning from their experience. It’s no surprise, therefore, that there has been an explosion in the number of machine learning companies worldwide. There are thousands of exciting machine learning startups in existence, with more being established all the time. Choosing a select few isn’t easy, but here we have picked out a variety of notable ML startups operating in different industry sectors.
1) DataRobot – predictive analytics
DataRobot provides a machine learning platform which enables data scientists to build and deploy predictive models. With the current digital talent gap, companies often struggle to recruit for many technological roles, and AI workers are no exception. Using machine learning to build predictive models not only makes it easier for less technically skilled employees to carry out predictive analytics, but also significantly speeds up the time required to do so. Data scientists simply input their data set into the DataRobot platform, from where they can automatically build and deploy various models to analyse their data. This makes the platform ideal for the growing number of citizen data scientists.
2) Graphcore – improving AI
Graphcore is one of the world’s most highly valued AI startups. In December 2018, the UK company reached a valuation of $1.7bn, after a $200m funding round featuring investors such as Microsoft and BMW. Graphcore’s technology is designed to lower the cost and improve the performance of artificial intelligence. According to the Bristol based startup, their Intelligence Processing Unit (IPU) is the first processor to be specifically designed for machine intelligence workloads. To complement this, Graphcore also offers proprietary software known as Poplar – a graph programming framework for machine learning programmes, which visualises the operations of the IPU.
3) Onfido – identity verification
Onfido was founded by three former students of Oxford University in 2012. The company enables people to verify their identity through a combination of document ID verification and facial recognition technology. By applying machine learning to a photograph of the user’s ID document (such as a passport or driving licence) and a photograph or video of their face, Onfido can detect if their documentation is real and if it matches the identity of the person. This process takes a matter of minutes, drastically improving the onboarding experience for new customers at security focused institutions such as banks.
4) Benevolent AI – pharmaceuticals
Benevolent AI is one of many startups disrupting the pharmaceutical industry. The company applies machine learning to improve the way that medicines are discovered, developed, tested and brought to market via several different steps. These include processing and modelling bioscience data, to give scientists hypotheses and ideas to explore; understanding the biology of a disease; finding the best responders for specific drug treatments in patients ahead of clinical trials; and designing molecules to ensure that drugs have the best chance of efficacy in patients. This not only speeds up the discovery and delivery of treatments, but also ensures they are more effective. Benevolent AI is based in London, with a research facility in Cambridge, and further offices in New York and Belgium. In 2018 the startup raised $115m, bringing up its funding total to more than $200m.
5) Hunters.AI – cybersecurity
Hunters.AI is an Israeli startup on a mission to protect the world from cybersecurity threats. Its AI-driven threat hunting technology constantly searches an organisation’s systems for security breaches, and identifies attacks as soon as they are attempted. This gives companies the best chance to mitigate damage and isolate any serious risks. The technology also provides a full report of any incidents which do occur, detailing the timeline, location, risk level, target and any recommended actions. Hunters.AI was founded in 2018 and raised $5.4m in its first seed round.
6) Pony.ai – autonomous vehicles
The age of autonomous vehicles (AVs) might be hotly anticipated, but there’s a lot of work to do before we get there. As a result, many AV startups have cropped up, whether it’s in individual fields such as mapping, perception and driver safety, or the creation of AVs as a whole. One company seeking to create a fully autonomous driving system is Pony.ai, which is based in Silicon Valley and Guangzhou, China. Since it was founded in December 2016, Pony.ai has already achieved several significant milestones. These include completing its first fully autonomous driving demo in 2017, and making AVs available to the public in China for the first time ever in 2018. In April 2019 the company also announced its test project for autonomous trucks.
7) Behavox – workplace behaviour
Behavox is an AI platform which transforms behaviour in the workplace. The startup applies machine learning to the data generated by its employees’ actions to discover patterns in their behaviour. This enables them to leverage insights to improve fields such as compliance, culture and motivation, productivity, and creativity. Whilst such insights are valuable in any industry, Behavox has been particularly designed with financial organisations in mind. Its platform can help to identify and limit the actions of rogue traders, by raising surveillance alerts at the account level instead of at the level of the broker. The startup has offices in London, Montreal, New York, Singapore, and San Francisco, and made it onto Forbes Fintech 50 for 2019.
8) Taranis – crop surveillance
Taranis is an agriculture intelligence platform that uses computer vision, data science and deep learning to monitor fields. The company’s aim is to prevent the loss of crop yields which frequently occur due to insects, disease, weeds, and nutrient deficiencies. It analyses aerial surveillance images from satellites, planes and drones to identify any crop health issues at the early stages. Taranis was founded in Israel in 2015, and currently oversees millions of acres of farmland in the United States, Argentina, Brazil, Russia, Ukraine and Australia.
9) SenseTime – facial recognition
SenseTime – an AI and machine learning company – is Hong Kong’s first tech unicorn, and one of the world’s most successful startups. In fact, since its founding in 2014, SenseTime has become the world’s most highly valued AI startup, with a valuation of more than $4.5bn. The company’s main remit lies in its proprietary platform for deep learning, which it uses to offer image, text and facial recognition, autonomous driving technology, remote sensing and video analysis. Its customers include smartphone companies Huawei and Xiaomi, tech giants Alibaba and Qualcomm, and public institutions such as the Massachusetts Institute of Technology in the USA.
10) Cleo – personal finance
Thanks to the Open Banking directive, consumers now have access to a host of third party financial products designed to help them manage their money. One provider of such products is Cleo, an intelligent personal financial assistant. Hosted on Facebook Messenger, the Cleo chatbot analyses a user’s transaction histories to assess their spending habits. It can therefore answer questions about how individuals are spending their money, as well as suggesting ways to save and deals on financial products. Cleo uses Natural Language Processing (NLP) – a branch of machine learning – to understand its interactions with people and communicate in an effective manner. Cleo was founded in London in 2016 and has raised $13.3m in funding since this time.
Machine learning techniques can be applied to all industries for business optimisation. They not only enable businesses to do existing things better, but also create new value chains and opportunities.
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